0
Research Papers

Effect of Condition Monitoring on Risk Mitigation for Steam Turbines in the Forest Products Industry

[+] Author and Article Information
Bin Zhou

Mem. ASME
Risk, Reliability and Failure Prevention Area,
FM Global Research,
1151 Boston-Providence Turnpike,
Norwood, MA 02062
e-mail: bin.zhou@fmglobal.com

Kumar Bhimavarapu

Risk, Reliability and Failure Prevention Area,
FM Global Research,
1151 Boston-Providence Turnpike,
Norwood, MA 02062
e-mail: kumar.bhimavarapu@fmglobal.com

1Corresponding author.

Manuscript received January 28, 2016; final manuscript received January 4, 2017; published online June 12, 2017. Assoc. Editor: Jeremy M. Gernand.

ASME J. Risk Uncertainty Part B 3(3), 031003 (Jun 12, 2017) (8 pages) Paper No: RISK-16-1009; doi: 10.1115/1.4035704 History: Received January 28, 2016; Revised January 04, 2017

Industry has been implementing condition monitoring (CM) for turbines to minimize losses and to improve productivity. Deficient conditions can be identified before losses occur by monitoring the equipment parameters. For any loss scenario, the effectiveness of monitoring depends on the stage of the loss scenario when the deficient condition is detected. A scenario-based semi-empirical methodology was developed to assess various types of condition monitoring techniques, by considering their effect on the risk associated with mechanical breakdown of steam turbines in the forest products (FPs) industry. A list of typical turbine loss scenarios was first generated by reviewing loss data and leveraging expert domain knowledge. Subsequently, condition monitoring techniques that can mitigate the risk associated with each loss scenario were identified. For each loss scenario, an event tree analysis (ETA) was used to quantitatively assess the variations in the outcomes due to condition monitoring, and resultant changes in the risk associated with turbine mechanical breakdown. An application was developed following the methodology to evaluate the effect of condition monitoring on turbine risk mitigation.

FIGURES IN THIS ARTICLE
<>
Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.

References

Latcovich, J. , 2013, “ When Should You Do Your Next Major Turbine Outage?,” The Locomotive, The Hartford Steam Boiler Inspection and Insurance Company, Hartford, CT.
Tanner, M. , 2013, “ Risk—What Does It Mean to Me?,” Conduit, 16(1).
Carazas, F. , and Souza, G. , 2009, “ Availability Analysis of Gas Turbines Used in Power Plants,” Int. J. Thermodyn., 12(1), pp. 28–37.
Fujiyama, K. , Nagai, S. , Akikuni, Y. , Fujiwara, T. , Furuya, K. , Matsumoto, S. , Takagi, K. , and Kawabata, T. , 2004, “ Risk-Based Inspection and Maintenance Systems for Steam Turbines,” Int. J. Pressure Vessels Piping, 81(10–11), pp. 825–835. [CrossRef]
Krishnasamy, L. , Khan, F. , and Haddara, M. , 2005, “ Development of Risk-Based Maintenance (RBM) Strategy for a Power-Generating Plant,” J. Loss Prev. Process Ind., 18(2), pp. 69–81. [CrossRef]
Roemer, M. J. , and Kacprzynski, G. J. , 2000, “ Advanced Diagnostics and Prognostics for Gas Turbine Engine Risk Assessment,” IEEE Aerospace Conference (AERO), Big Sky, MT, Mar. 18–25, pp. 345–353.
DePold, H. R. , and Gass, F. D. , 1999, “ The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics,” ASME J. Eng. Gas Turbines Power, 121(4), pp. 607–612. [CrossRef]
Heng, A. , Zhang, S. , Tan, C. C. , and Math, J. , 2009, “ Rotating Machinery Prognostics: State of the Art, Challenges and Opportunities,” Mech. Syst. Signal Process., 23(3), pp. 724–739. [CrossRef]
Jardine, K. S. , 2002, “ Optimizing Condition Based Maintenance Decisions,” IEEE Annual Reliability and Maintainability Symposium (RAMS), Seattle, WA, Jan. 28–31, pp. 90–97.
Jardine, K. S. , Lin, D. , and Banjevic, D. , 2006, “ A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance,” Mech. Syst. Signal Process., 20(7), pp. 1483–1510. [CrossRef]
Zhou, B. , 2016, “ Powergen Gas Turbine Losses and Condition Monitoring-A Loss Data Based Study,” ASME J. Risk Uncertainty Eng. Syst., 2(2), p. 021007.
Clemens, P. L. , and Rodney, J. S. , 1988, “ System Safety and Risk Management,” NIOSH Instructional Module, A Guide for Engineering Educators, National Institute for Occupational Safety and Health, Cincinnati, OH, pp. IX–3–IX–7.

Figures

Grahic Jump Location
Fig. 1

A schematic of loss scenario progression and CM

Grahic Jump Location
Fig. 2

A schematic event tree for risk evaluation with CM

Grahic Jump Location
Fig. 3

Share of loss value for groups of loss scenarios

Grahic Jump Location
Fig. 4

Share of loss count for groups of loss scenarios

Grahic Jump Location
Fig. 5

Severity and likelihood of loss scenarios for FP steam turbines

Grahic Jump Location
Fig. 6

Risk ratios for all loss scenarios

Grahic Jump Location
Fig. 7

Comparison of current and past risk levels for all loss scenarios

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Articles from Part A: Civil Engineering
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In